Page 246 - Big Data Analytics for Intelligent Healthcare Management

9.3 HEALTH RECOMMENDATION SYSTEM 239

information from various sources, including patients, doctors, hospitals, laboratory tests, CT scan,
X-ray, etc. If the new patient is admitted, then the whole process starts from the beginning that is, from
the processing of data and the creation of new patient health record. In the case of an existing patient,
the system updates the record as per requirements.

iii. Sentiment analysis
In order to support the user-based recommendation for the clinical services, it is imperative to make
sure the patients trust the whole system that is, system reliability to maintain privacy and confidentiality
of patient data. Information with or without adequate medicinal data obtained from patients is personal
and should not be misused.
iv. Recommendation
From the extraction of rules and user context, recommendations can be generated. Patients receive per-
sonalized recommendations. These recommendations can be regarding prevention and corrective mea-
sures, reasons for the causes of the disease, or further process of treatment.
v. Privacy preservation

The HRS requires the blending of various clinical information in order to enhance the recommendation
quality so that healthcare improves. Subsequently, ensuring the privacy of a patient’s information plays
a vital role in clinical research. In the proposed approach, the integrity of this information will be main-
tained while personal identity is effectively shielded [13, 30–32].

9.3.3 METHODS TO DESIGN HRS
A framework is made up of different tools that satisfy domain requirements and specific criteria of
specific applications. These tools presented in the framework first put the focus on customer require-
ments and ensure that clients’ requirements are met first. The first tool vital for designing the frame-
work is the use of participatory design. It refers to the active participation of stakeholders. Patients
should play an active role while designing the framework because patient feedback can improve
the whole system and remove gaps present in the current system. Therefore, feedback from the patient
is essential. When users draft the recommendation system, they keep in mind health-related issues, not
sales and marketing by pharmaceutical companies because these systems can become their personal
assistant helping them to overcome health issues that are significant to them. The most demanding part
is to hypothesize the existing framework to allow large-scale participation of patients and doctors.
These tools could help in treatment without requiring the direct intervention of doctors.
The second tool important to HRS is the use of differential privacy. Differential privacy maintains
data privacy and security, which is the main problem prevalent in a recommendation system. Here, it is
used for the sharing of a patient’s medical history without revealing patient identities. So, privacy
should be provided by the end user. Users are often unaware of privacy, which contradicts their
long-term interests. To implement an intelligent health recommendation system, privacy is important
for the users. The level of knowledge about privacy threats on the internet is so important that different
risk perceptions and level of digital competency are also related to technology. Users are much more
reluctant to share data in personal spaces.
The third tool to incorporate is adequate and proper communication. Communication is bidirec-
tional (to the user and to the recommendation system). Users should be able to communicate in an